Overview

Dataset statistics

Number of variables39
Number of observations430
Missing cells4199
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory131.1 KiB
Average record size in memory312.3 B

Variable types

Numeric11
Categorical10
Boolean18

Alerts

Age is highly correlated with BMI and 2 other fieldsHigh correlation
BMI is highly correlated with Age and 2 other fieldsHigh correlation
Height is highly correlated with Age and 2 other fieldsHigh correlation
Weight is highly correlated with Age and 2 other fieldsHigh correlation
AlvaradoScore is highly correlated with PediatricAppendicitisScore and 2 other fieldsHigh correlation
PediatricAppendicitisScore is highly correlated with AlvaradoScore and 1 other fieldsHigh correlation
BodyTemp is highly correlated with CRPEntryHigh correlation
WBCCount is highly correlated with AlvaradoScore and 1 other fieldsHigh correlation
NeutrophilPerc is highly correlated with AlvaradoScore and 2 other fieldsHigh correlation
CRPEntry is highly correlated with BodyTempHigh correlation
Age is highly correlated with Height and 1 other fieldsHigh correlation
BMI is highly correlated with WeightHigh correlation
Height is highly correlated with Age and 1 other fieldsHigh correlation
Weight is highly correlated with Age and 2 other fieldsHigh correlation
AlvaradoScore is highly correlated with PediatricAppendicitisScore and 2 other fieldsHigh correlation
PediatricAppendicitisScore is highly correlated with AlvaradoScore and 1 other fieldsHigh correlation
WBCCount is highly correlated with AlvaradoScore and 1 other fieldsHigh correlation
NeutrophilPerc is highly correlated with AlvaradoScore and 2 other fieldsHigh correlation
Age is highly correlated with Height and 1 other fieldsHigh correlation
BMI is highly correlated with WeightHigh correlation
Height is highly correlated with Age and 1 other fieldsHigh correlation
Weight is highly correlated with Age and 2 other fieldsHigh correlation
AlvaradoScore is highly correlated with PediatricAppendicitisScore and 2 other fieldsHigh correlation
PediatricAppendicitisScore is highly correlated with AlvaradoScoreHigh correlation
WBCCount is highly correlated with AlvaradoScoreHigh correlation
NeutrophilPerc is highly correlated with AlvaradoScoreHigh correlation
Enteritis is highly correlated with PathLymphNodesHigh correlation
MesentricLymphadenitis is highly correlated with PathLymphNodesHigh correlation
SurroundingTissueReaction is highly correlated with DiagnosisByCriteriaHigh correlation
PathLymphNodes is highly correlated with Enteritis and 1 other fieldsHigh correlation
AppendixWallLayers is highly correlated with TissuePerfusion and 1 other fieldsHigh correlation
TissuePerfusion is highly correlated with AppendixWallLayers and 3 other fieldsHigh correlation
AppendixOnSono is highly correlated with AppendixWallLayers and 2 other fieldsHigh correlation
Peritonitis is highly correlated with Ileus and 1 other fieldsHigh correlation
Ileus is highly correlated with PeritonitisHigh correlation
DiagnosisByCriteria is highly correlated with SurroundingTissueReaction and 3 other fieldsHigh correlation
Kokarde is highly correlated with TissuePerfusion and 1 other fieldsHigh correlation
LowerAbdominalPainRight is highly correlated with TissuePerfusionHigh correlation
Age is highly correlated with BMI and 3 other fieldsHigh correlation
BMI is highly correlated with Age and 1 other fieldsHigh correlation
Height is highly correlated with Age and 1 other fieldsHigh correlation
Weight is highly correlated with Age and 2 other fieldsHigh correlation
AlvaradoScore is highly correlated with PediatricAppendicitisScore and 2 other fieldsHigh correlation
PediatricAppendicitisScore is highly correlated with AlvaradoScore and 7 other fieldsHigh correlation
AppendixOnSono is highly correlated with AppendixDiameter and 4 other fieldsHigh correlation
AppendixDiameter is highly correlated with AppendixOnSono and 3 other fieldsHigh correlation
MigratoryPain is highly correlated with PediatricAppendicitisScoreHigh correlation
LowerAbdominalPainRight is highly correlated with PediatricAppendicitisScoreHigh correlation
CoughingPain is highly correlated with PediatricAppendicitisScoreHigh correlation
Nausea is highly correlated with PediatricAppendicitisScoreHigh correlation
AppetiteLoss is highly correlated with PediatricAppendicitisScoreHigh correlation
WBCCount is highly correlated with NeutrophilPerc and 3 other fieldsHigh correlation
NeutrophilPerc is highly correlated with AlvaradoScore and 4 other fieldsHigh correlation
KetonesInUrine is highly correlated with BowelWallThickHigh correlation
ErythrocytesInUrine is highly correlated with IleusHigh correlation
CRPEntry is highly correlated with WBCCount and 2 other fieldsHigh correlation
Peritonitis is highly correlated with AlvaradoScore and 3 other fieldsHigh correlation
AppendixWallLayers is highly correlated with PathLymphNodesHigh correlation
Kokarde is highly correlated with AppendixOnSono and 2 other fieldsHigh correlation
TissuePerfusion is highly correlated with Age and 3 other fieldsHigh correlation
SurroundingTissueReaction is highly correlated with AppendixOnSono and 4 other fieldsHigh correlation
PathLymphNodes is highly correlated with AppendixWallLayers and 2 other fieldsHigh correlation
MesentricLymphadenitis is highly correlated with PathLymphNodes and 1 other fieldsHigh correlation
BowelWallThick is highly correlated with KetonesInUrine and 1 other fieldsHigh correlation
Ileus is highly correlated with AppendixDiameter and 3 other fieldsHigh correlation
FecalImpaction is highly correlated with MeteorismHigh correlation
Meteorism is highly correlated with FecalImpactionHigh correlation
Enteritis is highly correlated with AppendixOnSono and 2 other fieldsHigh correlation
DiagnosisByCriteria is highly correlated with AppendixOnSono and 6 other fieldsHigh correlation
AppendixDiameter has 164 (38.1%) missing values Missing
PsoasSign has 37 (8.6%) missing values Missing
NeutrophilPerc has 45 (10.5%) missing values Missing
KetonesInUrine has 123 (28.6%) missing values Missing
ErythrocytesInUrine has 123 (28.6%) missing values Missing
WBCInUrine has 123 (28.6%) missing values Missing
CRPEntry has 6 (1.4%) missing values Missing
Dysuria has 19 (4.4%) missing values Missing
Stool has 6 (1.4%) missing values Missing
FreeFluids has 17 (4.0%) missing values Missing
AppendixWallLayers has 288 (67.0%) missing values Missing
Kokarde has 280 (65.1%) missing values Missing
TissuePerfusion has 375 (87.2%) missing values Missing
SurroundingTissueReaction has 250 (58.1%) missing values Missing
PathLymphNodes has 265 (61.6%) missing values Missing
MesentricLymphadenitis has 292 (67.9%) missing values Missing
BowelWallThick has 342 (79.5%) missing values Missing
Ileus has 361 (84.0%) missing values Missing
FecalImpaction has 356 (82.8%) missing values Missing
Meteorism has 323 (75.1%) missing values Missing
Enteritis has 389 (90.5%) missing values Missing
CRPEntry has 93 (21.6%) zeros Zeros

Reproduction

Analysis started2022-10-05 13:10:55.208625
Analysis finished2022-10-05 13:12:00.366407
Duration1 minute and 5.16 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct407
Distinct (%)95.1%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean11.36898297
Minimum0.03559206023
Maximum17.87268994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:00.765802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.03559206023
5-th percentile5.11156742
Q19.338809035
median11.52908966
Q313.92402464
95-th percentile16.76728268
Maximum17.87268994
Range17.83709788
Interquartile range (IQR)4.585215606

Descriptive statistics

Standard deviation3.421575431
Coefficient of variation (CV)0.3009570373
Kurtosis0.181485357
Mean11.36898297
Median Absolute Deviation (MAD)2.291581109
Skewness-0.5335179803
Sum4865.924709
Variance11.70717843
MonotonicityNot monotonic
2022-10-05T19:12:01.220228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.198494183
 
0.7%
14.622861052
 
0.5%
11.479808352
 
0.5%
15.214236822
 
0.5%
7.2114989732
 
0.5%
15.323750862
 
0.5%
15.813826152
 
0.5%
12.509240252
 
0.5%
9.2375085562
 
0.5%
16.783025332
 
0.5%
Other values (397)407
94.7%
ValueCountFrequency (%)
0.035592060231
0.2%
0.082135523611
0.2%
0.53388090351
0.2%
1.7275838471
0.2%
2.1327857631
0.2%
2.6338124571
0.2%
3.1923340181
0.2%
3.3264887061
0.2%
3.3675564681
0.2%
3.5674195761
0.2%
ValueCountFrequency (%)
17.872689941
0.2%
17.708418891
0.2%
17.522245041
0.2%
17.489390831
0.2%
17.456536621
0.2%
17.445585221
0.2%
17.371663241
0.2%
17.284052021
0.2%
17.20739221
0.2%
17.199178641
0.2%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct421
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.97640589
Minimum7.82798256
Maximum38.15622122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:01.721539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.82798256
5-th percentile13.70729024
Q115.84538933
median18.14369129
Q321.24462363
95-th percentile27.92962365
Maximum38.15622122
Range30.32823866
Interquartile range (IQR)5.399234304

Descriptive statistics

Standard deviation4.29595518
Coefficient of variation (CV)0.2263840269
Kurtosis1.444758372
Mean18.97640589
Median Absolute Deviation (MAD)2.63987775
Skewness1.033857438
Sum8159.854532
Variance18.45523091
MonotonicityNot monotonic
2022-10-05T19:12:02.169482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.37870052
 
0.5%
17.928215432
 
0.5%
16.885224332
 
0.5%
17.604772852
 
0.5%
20.775623272
 
0.5%
15.432098772
 
0.5%
15.065741422
 
0.5%
16.836734692
 
0.5%
15.99124692
 
0.5%
16.85803571
 
0.2%
Other values (411)411
95.6%
ValueCountFrequency (%)
7.827982561
0.2%
11.342155011
0.2%
11.897679951
0.2%
12.152777781
0.2%
12.191186491
0.2%
12.510164511
0.2%
12.551
0.2%
12.595221611
0.2%
12.599705331
0.2%
12.809088881
0.2%
ValueCountFrequency (%)
38.156221221
0.2%
35.37981271
0.2%
32.373113851
0.2%
32.183643811
0.2%
31.841831431
0.2%
30.863006831
0.2%
30.72664361
0.2%
30.629250931
0.2%
30.43509071
0.2%
29.93774711
0.2%

Sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
male
231 
female
199 

Length

Max length6
Median length4
Mean length4.925581395
Min length4

Characters and Unicode

Total characters2118
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
male231
53.7%
female199
46.3%

Length

2022-10-05T19:12:02.610636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:03.076286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male231
53.7%
female199
46.3%

Most occurring characters

ValueCountFrequency (%)
e629
29.7%
m430
20.3%
a430
20.3%
l430
20.3%
f199
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2118
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e629
29.7%
m430
20.3%
a430
20.3%
l430
20.3%
f199
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2118
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e629
29.7%
m430
20.3%
a430
20.3%
l430
20.3%
f199
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e629
29.7%
m430
20.3%
a430
20.3%
l430
20.3%
f199
 
9.4%

Height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct162
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.4209302
Minimum47.5
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:03.459277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum47.5
5-th percentile113.45
Q1138
median150.5
Q3162.875
95-th percentile175
Maximum190
Range142.5
Interquartile range (IQR)24.875

Descriptive statistics

Standard deviation19.95652569
Coefficient of variation (CV)0.1344589719
Kurtosis2.321375344
Mean148.4209302
Median Absolute Deviation (MAD)12.5
Skewness-1.023418679
Sum63821
Variance398.2629175
MonotonicityNot monotonic
2022-10-05T19:12:03.984263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16410
 
2.3%
15810
 
2.3%
1529
 
2.1%
1469
 
2.1%
1418
 
1.9%
1628
 
1.9%
1668
 
1.9%
1507
 
1.6%
1657
 
1.6%
1367
 
1.6%
Other values (152)347
80.7%
ValueCountFrequency (%)
47.51
0.2%
531
0.2%
83.51
0.2%
901
0.2%
941
0.2%
94.61
0.2%
951
0.2%
96.31
0.2%
1031
0.2%
1041
0.2%
ValueCountFrequency (%)
1901
 
0.2%
1851
 
0.2%
1841
 
0.2%
1831
 
0.2%
182.51
 
0.2%
1821
 
0.2%
181.51
 
0.2%
1814
0.9%
1802
0.5%
179.41
 
0.2%

Weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct208
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.63217442
Minimum3.275
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:04.516755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.275
5-th percentile18
Q131.125
median42
Q355
95-th percentile74.165
Maximum98
Range94.725
Interquartile range (IQR)23.875

Descriptive statistics

Standard deviation17.03825773
Coefficient of variation (CV)0.3904975618
Kurtosis-0.2278281279
Mean43.63217442
Median Absolute Deviation (MAD)12
Skewness0.3666633403
Sum18761.835
Variance290.3022263
MonotonicityNot monotonic
2022-10-05T19:12:05.044186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5012
 
2.8%
3310
 
2.3%
559
 
2.1%
537
 
1.6%
377
 
1.6%
567
 
1.6%
397
 
1.6%
476
 
1.4%
366
 
1.4%
346
 
1.4%
Other values (198)353
82.1%
ValueCountFrequency (%)
3.2751
 
0.2%
3.961
 
0.2%
121
 
0.2%
12.53
0.7%
12.71
 
0.2%
14.21
 
0.2%
152
0.5%
15.51
 
0.2%
15.81
 
0.2%
163
0.7%
ValueCountFrequency (%)
981
 
0.2%
94.11
 
0.2%
88.81
 
0.2%
871
 
0.2%
85.91
 
0.2%
852
0.5%
831
 
0.2%
801
 
0.2%
783
0.7%
77.71
 
0.2%

AlvaradoScore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723255814
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:05.456351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median6
Q37
95-th percentile9
Maximum10
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.142949323
Coefficient of variation (CV)0.374428366
Kurtosis-0.8544752845
Mean5.723255814
Median Absolute Deviation (MAD)2
Skewness-0.003872326201
Sum2461
Variance4.592231799
MonotonicityNot monotonic
2022-10-05T19:12:05.849697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
670
16.3%
766
15.3%
565
15.1%
457
13.3%
848
11.2%
339
9.1%
937
8.6%
236
8.4%
1012
 
2.8%
ValueCountFrequency (%)
236
8.4%
339
9.1%
457
13.3%
565
15.1%
670
16.3%
766
15.3%
848
11.2%
937
8.6%
1012
 
2.8%
ValueCountFrequency (%)
1012
 
2.8%
937
8.6%
848
11.2%
766
15.3%
670
16.3%
565
15.1%
457
13.3%
339
9.1%
236
8.4%

PediatricAppendicitisScore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.953488372
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:06.233655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.986592453
Coefficient of variation (CV)0.4010491807
Kurtosis-0.1516692434
Mean4.953488372
Median Absolute Deviation (MAD)1
Skewness0.4857780155
Sum2130
Variance3.946549574
MonotonicityNot monotonic
2022-10-05T19:12:06.536442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
491
21.2%
579
18.4%
677
17.9%
354
12.6%
245
10.5%
733
 
7.7%
819
 
4.4%
919
 
4.4%
1010
 
2.3%
13
 
0.7%
ValueCountFrequency (%)
13
 
0.7%
245
10.5%
354
12.6%
491
21.2%
579
18.4%
677
17.9%
733
 
7.7%
819
 
4.4%
919
 
4.4%
1010
 
2.3%
ValueCountFrequency (%)
1010
 
2.3%
919
 
4.4%
819
 
4.4%
733
 
7.7%
677
17.9%
579
18.4%
491
21.2%
354
12.6%
245
10.5%
13
 
0.7%

AppendixOnSono
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size988.0 B
True
276 
False
152 
(Missing)
 
2
ValueCountFrequency (%)
True276
64.2%
False152
35.3%
(Missing)2
 
0.5%
2022-10-05T19:12:06.899372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

AppendixDiameter
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)22.6%
Missing164
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean7.713157895
Minimum2.7
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:07.249890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4
Q16
median7.3
Q39.1
95-th percentile12
Maximum17
Range14.3
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.574918169
Coefficient of variation (CV)0.3338344947
Kurtosis0.06181936606
Mean7.713157895
Median Absolute Deviation (MAD)1.7
Skewness0.5508988765
Sum2051.7
Variance6.630203575
MonotonicityNot monotonic
2022-10-05T19:12:07.664808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024
 
5.6%
724
 
5.6%
824
 
5.6%
623
 
5.3%
918
 
4.2%
517
 
4.0%
1115
 
3.5%
1212
 
2.8%
411
 
2.6%
6.57
 
1.6%
Other values (50)91
21.2%
(Missing)164
38.1%
ValueCountFrequency (%)
2.71
 
0.2%
2.91
 
0.2%
31
 
0.2%
3.51
 
0.2%
3.72
 
0.5%
3.81
 
0.2%
411
2.6%
4.21
 
0.2%
4.32
 
0.5%
4.41
 
0.2%
ValueCountFrequency (%)
171
 
0.2%
152
 
0.5%
142
 
0.5%
13.21
 
0.2%
134
 
0.9%
1212
2.8%
11.81
 
0.2%
1115
3.5%
10.91
 
0.2%
10.71
 
0.2%

MigratoryPain
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size558.0 B
False
320 
True
110 
ValueCountFrequency (%)
False320
74.4%
True110
 
25.6%
2022-10-05T19:12:08.108023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

LowerAbdominalPainRight
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size988.0 B
True
416 
False
 
13
(Missing)
 
1
ValueCountFrequency (%)
True416
96.7%
False13
 
3.0%
(Missing)1
 
0.2%
2022-10-05T19:12:08.430231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)0.5%
Missing3
Missing (%)0.7%
Memory size988.0 B
False
280 
True
147 
(Missing)
 
3
ValueCountFrequency (%)
False280
65.1%
True147
34.2%
(Missing)3
 
0.7%
2022-10-05T19:12:08.770683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

CoughingPain
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size988.0 B
False
313 
True
116 
(Missing)
 
1
ValueCountFrequency (%)
False313
72.8%
True116
 
27.0%
(Missing)1
 
0.2%
2022-10-05T19:12:09.115516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

PsoasSign
Categorical

MISSING

Distinct2
Distinct (%)0.5%
Missing37
Missing (%)8.6%
Memory size3.5 KiB
negative
273 
positive
120 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3144
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownegative
2nd rownegative
3rd rownegative
4th rowpositive
5th rownegative

Common Values

ValueCountFrequency (%)
negative273
63.5%
positive120
27.9%
(Missing)37
 
8.6%

Length

2022-10-05T19:12:09.427591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:09.775020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
negative273
69.5%
positive120
30.5%

Most occurring characters

ValueCountFrequency (%)
e666
21.2%
i513
16.3%
t393
12.5%
v393
12.5%
n273
8.7%
g273
8.7%
a273
8.7%
p120
 
3.8%
o120
 
3.8%
s120
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3144
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e666
21.2%
i513
16.3%
t393
12.5%
v393
12.5%
n273
8.7%
g273
8.7%
a273
8.7%
p120
 
3.8%
o120
 
3.8%
s120
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin3144
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e666
21.2%
i513
16.3%
t393
12.5%
v393
12.5%
n273
8.7%
g273
8.7%
a273
8.7%
p120
 
3.8%
o120
 
3.8%
s120
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e666
21.2%
i513
16.3%
t393
12.5%
v393
12.5%
n273
8.7%
g273
8.7%
a273
8.7%
p120
 
3.8%
o120
 
3.8%
s120
 
3.8%

Nausea
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size558.0 B
True
242 
False
188 
ValueCountFrequency (%)
True242
56.3%
False188
43.7%
2022-10-05T19:12:10.144817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

AppetiteLoss
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size988.0 B
False
304 
True
125 
(Missing)
 
1
ValueCountFrequency (%)
False304
70.7%
True125
29.1%
(Missing)1
 
0.2%
2022-10-05T19:12:10.558954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

BodyTemp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)10.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.58969697
Minimum26.9
Maximum40.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:10.944295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum26.9
5-th percentile36.5
Q137
median37.4
Q338.2
95-th percentile39.2
Maximum40.2
Range13.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.9907314165
Coefficient of variation (CV)0.02635646191
Kurtosis30.68695987
Mean37.58969697
Median Absolute Deviation (MAD)0.6
Skewness-2.50148828
Sum16125.98
Variance0.9815487397
MonotonicityNot monotonic
2022-10-05T19:12:12.084994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
36.851
 
11.9%
3747
 
10.9%
37.245
 
10.5%
37.833
 
7.7%
38.224
 
5.6%
37.522
 
5.1%
3816
 
3.7%
37.415
 
3.5%
38.413
 
3.0%
3913
 
3.0%
Other values (33)150
34.9%
ValueCountFrequency (%)
26.91
 
0.2%
365
 
1.2%
36.28
 
1.9%
36.31
 
0.2%
36.46
 
1.4%
36.57
 
1.6%
36.68
 
1.9%
36.71
 
0.2%
36.851
11.9%
36.94
 
0.9%
ValueCountFrequency (%)
40.21
 
0.2%
402
0.5%
39.91
 
0.2%
39.84
0.9%
39.71
 
0.2%
39.61
 
0.2%
39.54
0.9%
39.43
0.7%
39.32
0.5%
39.24
0.9%

WBCCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct173
Distinct (%)40.6%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean12.42934272
Minimum2.6
Maximum33.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:12.498163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile5.7
Q18.4
median11.85
Q315.8
95-th percentile21.85
Maximum33.6
Range31
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation5.279407992
Coefficient of variation (CV)0.4247535939
Kurtosis0.9621865857
Mean12.42934272
Median Absolute Deviation (MAD)3.7
Skewness0.8698216707
Sum5294.9
Variance27.87214874
MonotonicityNot monotonic
2022-10-05T19:12:12.957894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78
 
1.9%
12.47
 
1.6%
8.77
 
1.6%
10.96
 
1.4%
156
 
1.4%
8.66
 
1.4%
7.56
 
1.4%
8.96
 
1.4%
18.76
 
1.4%
13.16
 
1.4%
Other values (163)362
84.2%
ValueCountFrequency (%)
2.61
0.2%
3.52
0.5%
41
0.2%
4.11
0.2%
4.21
0.2%
4.31
0.2%
4.41
0.2%
4.51
0.2%
4.61
0.2%
4.71
0.2%
ValueCountFrequency (%)
33.61
0.2%
33.31
0.2%
30.51
0.2%
29.91
0.2%
28.21
0.2%
27.51
0.2%
27.31
0.2%
25.71
0.2%
25.21
0.2%
251
0.2%

NeutrophilPerc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct258
Distinct (%)67.0%
Missing45
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean70.87350649
Minimum27.2
Maximum94.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:13.389573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27.2
5-th percentile43.62
Q159.1
median74.9
Q382.9
95-th percentile88.48
Maximum94.1
Range66.9
Interquartile range (IQR)23.8

Descriptive statistics

Standard deviation14.23848491
Coefficient of variation (CV)0.2008999641
Kurtosis-0.6500872413
Mean70.87350649
Median Absolute Deviation (MAD)9.5
Skewness-0.6084973287
Sum27286.3
Variance202.7344525
MonotonicityNot monotonic
2022-10-05T19:12:13.854797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
846
 
1.4%
84.55
 
1.2%
795
 
1.2%
74.94
 
0.9%
85.54
 
0.9%
814
 
0.9%
663
 
0.7%
68.43
 
0.7%
43.53
 
0.7%
803
 
0.7%
Other values (248)345
80.2%
(Missing)45
 
10.5%
ValueCountFrequency (%)
27.21
0.2%
35.11
0.2%
36.11
0.2%
38.71
0.2%
39.51
0.2%
401
0.2%
40.61
0.2%
41.11
0.2%
41.22
0.5%
41.61
0.2%
ValueCountFrequency (%)
94.11
0.2%
93.21
0.2%
92.41
0.2%
92.11
0.2%
91.91
0.2%
91.81
0.2%
91.61
0.2%
91.11
0.2%
90.61
0.2%
90.51
0.2%

KetonesInUrine
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.3%
Missing123
Missing (%)28.6%
Memory size3.5 KiB
no
189 
+++
64 
+
36 
++
 
18

Length

Max length3
Median length2
Mean length2.091205212
Min length1

Characters and Unicode

Total characters642
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row+
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no189
44.0%
+++64
 
14.9%
+36
 
8.4%
++18
 
4.2%
(Missing)123
28.6%

Length

2022-10-05T19:12:14.257797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:14.640537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no189
61.6%
118
38.4%

Most occurring characters

ValueCountFrequency (%)
+264
41.1%
n189
29.4%
o189
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter378
58.9%
Math Symbol264
41.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n189
50.0%
o189
50.0%
Math Symbol
ValueCountFrequency (%)
+264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin378
58.9%
Common264
41.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n189
50.0%
o189
50.0%
Common
ValueCountFrequency (%)
+264
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+264
41.1%
n189
29.4%
o189
29.4%

ErythrocytesInUrine
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.3%
Missing123
Missing (%)28.6%
Memory size3.5 KiB
no
239 
+
40 
+++
 
18
++
 
10

Length

Max length3
Median length2
Mean length1.928338762
Min length1

Characters and Unicode

Total characters592
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row+
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no239
55.6%
+40
 
9.3%
+++18
 
4.2%
++10
 
2.3%
(Missing)123
28.6%

Length

2022-10-05T19:12:14.993506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:15.387141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no239
77.9%
68
 
22.1%

Most occurring characters

ValueCountFrequency (%)
n239
40.4%
o239
40.4%
+114
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter478
80.7%
Math Symbol114
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n239
50.0%
o239
50.0%
Math Symbol
ValueCountFrequency (%)
+114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin478
80.7%
Common114
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n239
50.0%
o239
50.0%
Common
ValueCountFrequency (%)
+114
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n239
40.4%
o239
40.4%
+114
19.3%

WBCInUrine
Categorical

MISSING

Distinct4
Distinct (%)1.3%
Missing123
Missing (%)28.6%
Memory size3.5 KiB
no
269 
+
 
23
++
 
8
+++
 
7

Length

Max length3
Median length2
Mean length1.947882736
Min length1

Characters and Unicode

Total characters598
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no269
62.6%
+23
 
5.3%
++8
 
1.9%
+++7
 
1.6%
(Missing)123
28.6%

Length

2022-10-05T19:12:15.729783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:16.102427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no269
87.6%
38
 
12.4%

Most occurring characters

ValueCountFrequency (%)
n269
45.0%
o269
45.0%
+60
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter538
90.0%
Math Symbol60
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n269
50.0%
o269
50.0%
Math Symbol
ValueCountFrequency (%)
+60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin538
90.0%
Common60
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n269
50.0%
o269
50.0%
Common
ValueCountFrequency (%)
+60
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n269
45.0%
o269
45.0%
+60
 
10.0%

CRPEntry
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct106
Distinct (%)25.0%
Missing6
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean30.6884434
Minimum0
Maximum365
Zeros93
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-10-05T19:12:16.495514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q331.25
95-th percentile151.55
Maximum365
Range365
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation56.65226461
Coefficient of variation (CV)1.846045558
Kurtosis10.6533497
Mean30.6884434
Median Absolute Deviation (MAD)7
Skewness3.056308507
Sum13011.9
Variance3209.479086
MonotonicityNot monotonic
2022-10-05T19:12:16.992646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
093
21.6%
143
 
10.0%
218
 
4.2%
614
 
3.3%
412
 
2.8%
312
 
2.8%
511
 
2.6%
1311
 
2.6%
710
 
2.3%
89
 
2.1%
Other values (96)191
44.4%
ValueCountFrequency (%)
093
21.6%
143
10.0%
218
 
4.2%
312
 
2.8%
412
 
2.8%
511
 
2.6%
614
 
3.3%
6.91
 
0.2%
710
 
2.3%
89
 
2.1%
ValueCountFrequency (%)
3651
0.2%
3551
0.2%
3391
0.2%
2931
0.2%
2781
0.2%
2451
0.2%
2361
0.2%
2351
0.2%
2341
0.2%
2101
0.2%

Dysuria
Boolean

MISSING

Distinct2
Distinct (%)0.5%
Missing19
Missing (%)4.4%
Memory size988.0 B
False
389 
True
 
22
(Missing)
 
19
ValueCountFrequency (%)
False389
90.5%
True22
 
5.1%
(Missing)19
 
4.4%
2022-10-05T19:12:17.409210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Stool
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing6
Missing (%)1.4%
Memory size3.5 KiB
normal
306 
diarrhea
67 
obstipation
51 

Length

Max length11
Median length6
Mean length6.91745283
Min length6

Characters and Unicode

Total characters2933
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rowobstipation

Common Values

ValueCountFrequency (%)
normal306
71.2%
diarrhea67
 
15.6%
obstipation51
 
11.9%
(Missing)6
 
1.4%

Length

2022-10-05T19:12:17.747751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:18.148798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
normal306
72.2%
diarrhea67
 
15.8%
obstipation51
 
12.0%

Most occurring characters

ValueCountFrequency (%)
a491
16.7%
r440
15.0%
o408
13.9%
n357
12.2%
m306
10.4%
l306
10.4%
i169
 
5.8%
t102
 
3.5%
d67
 
2.3%
h67
 
2.3%
Other values (4)220
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2933
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a491
16.7%
r440
15.0%
o408
13.9%
n357
12.2%
m306
10.4%
l306
10.4%
i169
 
5.8%
t102
 
3.5%
d67
 
2.3%
h67
 
2.3%
Other values (4)220
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2933
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a491
16.7%
r440
15.0%
o408
13.9%
n357
12.2%
m306
10.4%
l306
10.4%
i169
 
5.8%
t102
 
3.5%
d67
 
2.3%
h67
 
2.3%
Other values (4)220
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a491
16.7%
r440
15.0%
o408
13.9%
n357
12.2%
m306
10.4%
l306
10.4%
i169
 
5.8%
t102
 
3.5%
d67
 
2.3%
h67
 
2.3%
Other values (4)220
7.5%

Peritonitis
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
no
265 
local
150 
generalised
 
15

Length

Max length11
Median length2
Mean length3.360465116
Min length2

Characters and Unicode

Total characters1445
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowlocal
5th rowno

Common Values

ValueCountFrequency (%)
no265
61.6%
local150
34.9%
generalised15
 
3.5%

Length

2022-10-05T19:12:18.596746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:18.982010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no265
61.6%
local150
34.9%
generalised15
 
3.5%

Most occurring characters

ValueCountFrequency (%)
o415
28.7%
l315
21.8%
n280
19.4%
a165
 
11.4%
c150
 
10.4%
e45
 
3.1%
g15
 
1.0%
r15
 
1.0%
i15
 
1.0%
s15
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1445
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o415
28.7%
l315
21.8%
n280
19.4%
a165
 
11.4%
c150
 
10.4%
e45
 
3.1%
g15
 
1.0%
r15
 
1.0%
i15
 
1.0%
s15
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1445
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o415
28.7%
l315
21.8%
n280
19.4%
a165
 
11.4%
c150
 
10.4%
e45
 
3.1%
g15
 
1.0%
r15
 
1.0%
i15
 
1.0%
s15
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o415
28.7%
l315
21.8%
n280
19.4%
a165
 
11.4%
c150
 
10.4%
e45
 
3.1%
g15
 
1.0%
r15
 
1.0%
i15
 
1.0%
s15
 
1.0%

FreeFluids
Boolean

MISSING

Distinct2
Distinct (%)0.5%
Missing17
Missing (%)4.0%
Memory size988.0 B
False
233 
True
180 
(Missing)
 
17
ValueCountFrequency (%)
False233
54.2%
True180
41.9%
(Missing)17
 
4.0%
2022-10-05T19:12:19.396304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

AppendixWallLayers
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.4%
Missing288
Missing (%)67.0%
Memory size3.5 KiB
intakt
91 
aufgehoben
51 

Length

Max length10
Median length6
Mean length7.436619718
Min length6

Characters and Unicode

Total characters1056
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaufgehoben
2nd rowaufgehoben
3rd rowintakt
4th rowintakt
5th rowaufgehoben

Common Values

ValueCountFrequency (%)
intakt91
 
21.2%
aufgehoben51
 
11.9%
(Missing)288
67.0%

Length

2022-10-05T19:12:19.789908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:20.193936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
intakt91
64.1%
aufgehoben51
35.9%

Most occurring characters

ValueCountFrequency (%)
t182
17.2%
n142
13.4%
a142
13.4%
e102
9.7%
i91
8.6%
k91
8.6%
u51
 
4.8%
f51
 
4.8%
g51
 
4.8%
h51
 
4.8%
Other values (2)102
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1056
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t182
17.2%
n142
13.4%
a142
13.4%
e102
9.7%
i91
8.6%
k91
8.6%
u51
 
4.8%
f51
 
4.8%
g51
 
4.8%
h51
 
4.8%
Other values (2)102
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t182
17.2%
n142
13.4%
a142
13.4%
e102
9.7%
i91
8.6%
k91
8.6%
u51
 
4.8%
f51
 
4.8%
g51
 
4.8%
h51
 
4.8%
Other values (2)102
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t182
17.2%
n142
13.4%
a142
13.4%
e102
9.7%
i91
8.6%
k91
8.6%
u51
 
4.8%
f51
 
4.8%
g51
 
4.8%
h51
 
4.8%
Other values (2)102
9.7%

Kokarde
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.3%
Missing280
Missing (%)65.1%
Memory size988.0 B
False
81 
True
69 
(Missing)
280 
ValueCountFrequency (%)
False81
 
18.8%
True69
 
16.0%
(Missing)280
65.1%
2022-10-05T19:12:20.565355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

TissuePerfusion
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)5.5%
Missing375
Missing (%)87.2%
Memory size3.5 KiB
hyperperfused
20 
unremarkable
19 
hypoperfused
16 

Length

Max length13
Median length12
Mean length12.36363636
Min length12

Characters and Unicode

Total characters680
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhyperperfused
2nd rowunremarkable
3rd rowhypoperfused
4th rowhypoperfused
5th rowunremarkable

Common Values

ValueCountFrequency (%)
hyperperfused20
 
4.7%
unremarkable19
 
4.4%
hypoperfused16
 
3.7%
(Missing)375
87.2%

Length

2022-10-05T19:12:20.969625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:21.476651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
hyperperfused20
36.4%
unremarkable19
34.5%
hypoperfused16
29.1%

Most occurring characters

ValueCountFrequency (%)
e130
19.1%
r94
13.8%
p72
10.6%
u55
8.1%
a38
 
5.6%
h36
 
5.3%
y36
 
5.3%
f36
 
5.3%
s36
 
5.3%
d36
 
5.3%
Other values (6)111
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter680
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e130
19.1%
r94
13.8%
p72
10.6%
u55
8.1%
a38
 
5.6%
h36
 
5.3%
y36
 
5.3%
f36
 
5.3%
s36
 
5.3%
d36
 
5.3%
Other values (6)111
16.3%

Most occurring scripts

ValueCountFrequency (%)
Latin680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e130
19.1%
r94
13.8%
p72
10.6%
u55
8.1%
a38
 
5.6%
h36
 
5.3%
y36
 
5.3%
f36
 
5.3%
s36
 
5.3%
d36
 
5.3%
Other values (6)111
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e130
19.1%
r94
13.8%
p72
10.6%
u55
8.1%
a38
 
5.6%
h36
 
5.3%
y36
 
5.3%
f36
 
5.3%
s36
 
5.3%
d36
 
5.3%
Other values (6)111
16.3%

SurroundingTissueReaction
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.1%
Missing250
Missing (%)58.1%
Memory size988.0 B
True
129 
False
51 
(Missing)
250 
ValueCountFrequency (%)
True129
30.0%
False51
 
11.9%
(Missing)250
58.1%
2022-10-05T19:12:21.929025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

PathLymphNodes
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.2%
Missing265
Missing (%)61.6%
Memory size988.0 B
True
113 
False
52 
(Missing)
265 
ValueCountFrequency (%)
True113
26.3%
False52
 
12.1%
(Missing)265
61.6%
2022-10-05T19:12:22.375438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

MesentricLymphadenitis
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.4%
Missing292
Missing (%)67.9%
Memory size988.0 B
True
111 
False
 
27
(Missing)
292 
ValueCountFrequency (%)
True111
 
25.8%
False27
 
6.3%
(Missing)292
67.9%
2022-10-05T19:12:22.758877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

BowelWallThick
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.3%
Missing342
Missing (%)79.5%
Memory size988.0 B
False
52 
True
36 
(Missing)
342 
ValueCountFrequency (%)
False52
 
12.1%
True36
 
8.4%
(Missing)342
79.5%
2022-10-05T19:12:23.111745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Ileus
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.9%
Missing361
Missing (%)84.0%
Memory size988.0 B
False
59 
True
 
10
(Missing)
361 
ValueCountFrequency (%)
False59
 
13.7%
True10
 
2.3%
(Missing)361
84.0%
2022-10-05T19:12:23.497994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

FecalImpaction
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.7%
Missing356
Missing (%)82.8%
Memory size988.0 B
False
46 
True
 
28
(Missing)
356 
ValueCountFrequency (%)
False46
 
10.7%
True28
 
6.5%
(Missing)356
82.8%
2022-10-05T19:12:24.047148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Meteorism
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)1.9%
Missing323
Missing (%)75.1%
Memory size988.0 B
True
78 
False
 
29
(Missing)
323 
ValueCountFrequency (%)
True78
 
18.1%
False29
 
6.7%
(Missing)323
75.1%
2022-10-05T19:12:24.463453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Enteritis
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)4.9%
Missing389
Missing (%)90.5%
Memory size988.0 B
False
 
22
True
 
19
(Missing)
389 
ValueCountFrequency (%)
False22
 
5.1%
True19
 
4.4%
(Missing)389
90.5%
2022-10-05T19:12:24.798382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

DiagnosisByCriteria
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
appendicitis
246 
noAppendicitis
184 

Length

Max length14
Median length12
Mean length12.85581395
Min length12

Characters and Unicode

Total characters5528
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownoAppendicitis
2nd rowappendicitis
3rd rownoAppendicitis
4th rowappendicitis
5th rownoAppendicitis

Common Values

ValueCountFrequency (%)
appendicitis246
57.2%
noAppendicitis184
42.8%

Length

2022-10-05T19:12:25.169445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T19:12:25.532353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
appendicitis246
57.2%
noappendicitis184
42.8%

Most occurring characters

ValueCountFrequency (%)
i1290
23.3%
p860
15.6%
n614
11.1%
e430
 
7.8%
d430
 
7.8%
c430
 
7.8%
t430
 
7.8%
s430
 
7.8%
a246
 
4.5%
o184
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5344
96.7%
Uppercase Letter184
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1290
24.1%
p860
16.1%
n614
11.5%
e430
 
8.0%
d430
 
8.0%
c430
 
8.0%
t430
 
8.0%
s430
 
8.0%
a246
 
4.6%
o184
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
A184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5528
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1290
23.3%
p860
15.6%
n614
11.1%
e430
 
7.8%
d430
 
7.8%
c430
 
7.8%
t430
 
7.8%
s430
 
7.8%
a246
 
4.5%
o184
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1290
23.3%
p860
15.6%
n614
11.1%
e430
 
7.8%
d430
 
7.8%
c430
 
7.8%
t430
 
7.8%
s430
 
7.8%
a246
 
4.5%
o184
 
3.3%

Interactions

2022-10-05T19:11:49.139243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:12.166085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:15.285544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:23.225024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:26.430453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:29.662437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:33.446674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:36.570554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:39.736385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:42.803338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:45.811414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:49.566197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:12.577144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:15.702677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:23.627048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:26.655146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:30.029827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:33.833060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:36.810575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:40.073093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:43.053776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:46.080711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:49.954168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:12.815927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:20.626000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:23.905837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:26.912282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:30.342880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:34.102578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:37.034817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:40.406809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:43.313624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:46.374789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:50.297320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:13.139924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:20.939132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:24.175829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:27.178313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:30.617100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:34.333032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:37.315484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:40.677937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:43.626275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:46.626137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:50.948076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:13.409492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:21.215349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:24.456980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:27.430844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:30.898681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:34.628380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:37.562663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:40.945745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:43.858679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:46.913834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:51.594088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:13.689771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:21.526170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:24.706461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:27.763965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:31.183897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:34.921623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:37.843301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:41.226723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:44.102759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:47.158847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:52.007943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:13.956263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:21.765529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:24.946768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:28.117727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:31.491776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:35.159966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:38.082677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:41.487836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:44.387216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:47.414203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:52.420746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:14.215414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:22.021080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:25.227716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:28.362822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:31.862261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:35.446043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:38.402092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:41.728132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:44.629249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:47.728883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:52.761276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:14.477416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:22.312020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:25.495560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:28.619773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:32.248844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:35.716688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:38.630266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:41.966772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:44.899051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:48.054041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:53.024351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:14.759968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:22.585618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:25.820904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:29.133705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:32.626983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:35.985728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:38.861858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:42.265103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:45.203438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:48.360862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:53.639552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:15.019496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:22.824999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:26.088944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:29.399626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:33.030101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:36.262429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:39.121845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:42.573057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:45.521239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-05T19:11:48.754639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-10-05T19:12:25.893568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-05T19:12:26.529585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-05T19:12:27.113566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-05T19:12:27.771036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-05T19:12:29.018024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-05T19:11:54.389621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-05T19:11:56.919813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-05T19:11:58.307899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-05T19:11:59.749199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

AgeBMISexHeightWeightAlvaradoScorePediatricAppendicitisScoreAppendixOnSonoAppendixDiameterMigratoryPainLowerAbdominalPainRightReboundTendernessCoughingPainPsoasSignNauseaAppetiteLossBodyTempWBCCountNeutrophilPercKetonesInUrineErythrocytesInUrineWBCInUrineCRPEntryDysuriaStoolPeritonitisFreeFluidsAppendixWallLayersKokardeTissuePerfusionSurroundingTissueReactionPathLymphNodesMesentricLymphadenitisBowelWallThickIleusFecalImpactionMeteorismEnteritisDiagnosisByCriteria
012.53114316.494601male159.041.775yes5.5noyesnononegativeyesno38.713.366.0++no76.0nonormalnonoaufgehobennohyperperfusedyesyesyesyesnoyesnononoAppendicitis
112.41067812.595222female152.029.188yesNaNnoyesyesyesnegativeyesno38.814.993.2NaNNaNNaN10.0nonormalnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNappendicitis
210.53798815.991247male133.528.533yes6.0noyesnononegativenoyes37.26.655.3nonono13.0NaNnormalnonoaufgehobenyesNaNnonoNaNnonoNaNNaNnonoAppendicitis
310.42573616.185025male146.034.543yes6.0noyesnonoNaNnono37.012.457.9nonono6.0NaNnormallocalnoNaNNaNNaNnonoNaNnononononoappendicitis
413.27036320.449137female164.055.022yes6.5noyesnonopositivenono37.24.250.6nonono2.0noobstipationnonoNaNnoNaNyesnononononoyesnonoAppendicitis
57.40041115.200000male123.023.097yes7.0yesyesyesnonegativenoyes38.015.084.5NaNNaNNaN6.0NaNobstipationnoyesintaktNaNNaNnonononononoyesnoappendicitis
613.21560623.597004female171.069.033yes6.2yesyesnonopositivenono36.26.752.7nonono3.0yesdiarrheanonointaktnoNaNnononoNaNNaNNaNNaNNaNnoAppendicitis
713.10609222.070312female160.056.532noNaNnoyesnonopositivenono36.57.053.9+++no+0.0nonormalnonoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNappendicitis
811.43600317.941817male153.042.066yes10.2yesyesNaNyespositivenono37.813.458.6nonono18.0yesdiarrheanonoaufgehobennoNaNyesnononononoyesNaNappendicitis
915.12388823.602255female160.560.886yes7.3yesyesyesnoNaNyesno38.713.073.0nonono19.0NaNdiarrheanoyesintaktnoNaNnoyesyesnononononoappendicitis

Last rows

AgeBMISexHeightWeightAlvaradoScorePediatricAppendicitisScoreAppendixOnSonoAppendixDiameterMigratoryPainLowerAbdominalPainRightReboundTendernessCoughingPainPsoasSignNauseaAppetiteLossBodyTempWBCCountNeutrophilPercKetonesInUrineErythrocytesInUrineWBCInUrineCRPEntryDysuriaStoolPeritonitisFreeFluidsAppendixWallLayersKokardeTissuePerfusionSurroundingTissueReactionPathLymphNodesMesentricLymphadenitisBowelWallThickIleusFecalImpactionMeteorismEnteritisDiagnosisByCriteria
4206.18206714.863258male116.020.076noNaNnoyesnononegativeyesno38.917.585.5nonono50.0nonormallocalnoNaNNaNNaNNaNyesyesNaNNaNNaNNaNyesnoAppendicitis
42110.90212221.165279male145.044.556noNaNyesyesyesyespositiveyesno36.49.756.2nonono0.0nodiarrheanonoNaNnoNaNnoNaNNaNNaNNaNNaNNaNNaNnoAppendicitis
42217.19917922.367347female175.068.575noNaNnoyesnonopositiveyesno37.615.985.5no+++no0.0yesnormalnonoNaNNaNNaNNaNNaNNaNNaNNaNNaNyesNaNnoAppendicitis
4235.41820715.704719male112.019.765noNaNyesyesnononegativenono39.08.880.0+++nono76.0noobstipationnoyesNaNNaNNaNNaNNaNNaNyesNaNNaNNaNNaNnoAppendicitis
42415.48802220.581591female167.057.475noNaNnoyesyesnopositivenono38.012.285.4+++nono55.0nonormalnoyesNaNnoNaNNaNNaNNaNNaNNaNNaNyesNaNnoAppendicitis
42512.14784422.292563male166.561.854noNaNnoyesnononegativenono38.410.3NaNnonono1.0nodiarrheanonoNaNnoNaNnoyesyesnoNaNNaNNaNNaNnoAppendicitis
42612.52840529.316297male152.368.077noNaNyesyesyesyespositivenono36.811.082.4NaNNaNNaN5.0nonormalnonoNaNNaNNaNNaNNaNNaNNaNNaNyesNaNNaNnoAppendicitis
42712.01368928.906250male160.074.056noNaNyesyesyesyesNaNyesno37.07.556.3NaNNaNNaN1.0nonormalnonoNaNNaNNaNnononoNaNNaNNaNyesNaNnoAppendicitis
4287.73990422.038188female120.532.053noNaNnoyesyesnonegativenono38.89.854.5++++55.0noobstipationnonoNaNNaNNaNNaNyesyesNaNnoNaNyesyesnoAppendicitis
42910.15742621.017920female142.242.596noNaNyesyesyesnonegativeyesno37.415.072.5nonono2.0NaNNaNnonoNaNnoNaNNaNNaNNaNNaNNaNNaNNaNNaNnoAppendicitis